Automatic clustering of tokens from a corpus for grammar acquisition

ABSTRACT

In a method of learning grammar from a corpus, context words are identified from a corpus. For the other non-context words, the method counts the occurrence of predetermined relationships which the context words, and maps the counted occurrences to a multidimensional frequency space. Clusters are grown from the frequency vectors. The clusters represent classes of words; words in the same cluster possess the same lexical significancy and provide an indicator of grammatical structure.

BACKGROUND

The present invention relates to an application that builds linguistic models from a corpus of speech.

For a machine to comprehend speech, not only must the machine identify spoken (or typed) words, but it also must understand language grammar to comprehend the meaning of commands. Accordingly, much research has been devoted to the construction of language models that a machine may use to ascribe meaning to spoken commands. Often, language models are preprogrammed. However, such predefined models increase the costs of a speech recognition system. Also, the language models obtained therefrom have narrow applications. Unless a programmer predefines the language model to recognize a certain command, the speech recognition system that uses the model may not recognize the command. What is needed is a training system that automatically extracts grammatical relationships from a predefined corpus of speech.

SUMMARY

An embodiment of the present invention provides a method of learning grammar from a corpus, in which context words are identified from a corpus. For the other non-context words, the method counts the occurrence of predetermined relationships with the context words, and maps the counted occurrences to a multidimensional frequency space. Clusters are grown from the frequency vectors. The clusters represent classes of words; words in the same cluster possess the same lexical significancy and provide an indicator of grammatical structure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram of a method of an embodiment of the present invention.

FIG. 2 illustrates mapping frequency vectors that may be obtained during operation of the present invention.

FIG. 3 illustrates an exemplary cluster tree.

DETAILED DESCRIPTION

Embodiments of the present invention provide a system that automatically builds a grammatical model from a corpus of speech. The present invention uses clustering to group words and/or phrases according to their lexical significance. Relationships between high frequency words called “context words” and other input words are identified. The words to be clustered are each represented as a feature vector constructed from the identified relationships. Similarities between two input words are measured in terms of the distance between their feature vectors. Using these distances, input words are clustered according to a hierarchy. The hierarchy is then cut at a certain depth to produce clusters which are then ranked by a “goodness” metric. Those clusters that remain identify words or tokens from the corpus that possess similar grammatical significance.

Clustering per se is known. In the context of language modeling, clustering has typically been used on words to induce classes that are then used to predict smoothed probabilities of occurrence for rare or unseen events in the training corpus. Most clustering schemes use the average entropy reduction to decide when two words fall into the same cluster. Prior use of clustering, however, does not provide insight into language model of grammar.

FIG. 1 illustrates a method of the present invention according to a first embodiment. The method operates upon input text, a set of words from which the grammar model shall be constructed. Typically, the input text comprises a set of single words or phonemes. From the input text, the method identifies context words (Step 1010). Context words are those words or phonemes in the input text that occur with the highest frequency. The method 1000 may cause a predetermined number of words (say, 50) that occur with the highest frequency to be identified as context words.

The method 1000 determines relationships that may exist between the context words and the remaining words, called “input words” herein, in the input text. For example, the method 1000 may determine how many times and in which positions an input word appears adjacent to a context word. Table 1 below illustrates relationships that may exist between certain exemplary input words and exemplary context words.

TABLE 1 Context Word to from in Input Word −2 −1 1 2 −2 −1 1 2 −2 −1 1 2 Chicago f₁₁₁ f₁₁₂ f₁₁₃ f₁₁₄ f₁₂₁ f₁₂₂ f₁₂₃ f₁₂₄ f₁₃₁ f₁₃₂ f₁₃₃ f₁₃₄ New York f₂₁₁ f₂₁₂ f₂₁₃ f₂₁₄ f₂₂₁ f₂₂₂ f₂₂₃ f₂₂₄ f₂₃₁ f₂₃₂ f₂₃₃ f₂₃₄ Baltimore f₃₁₁ f₃₁₂ f₃₁₃ f₃₁₄ f₃₂₁ f₃₂₂ f₃₂₃ f₃₂₄ f₃₃₁ f₃₃₂ f₃₃₃ f₃₃₄ red f₄₁₁ f₄₁₂ f₄₁₃ f₄₁₄ f₄₂₁ f₄₂₂ f₄₂₃ f₄₂₄ f₄₃₁ f₄₃₂ f₄₃₃ f₄₃₄ white f₅₁₁ f₅₁₂ f₅₁₃ f₅₁₄ f₅₂₁ f₅₂₂ f₅₂₃ f₅₂₄ f₅₃₁ f₅₃₂ f₅₃₃ f₅₃₄ blue f₆₁₁ f₆₁₂ f₆₁₃ f₆₁₄ f₆₂₁ f₆₂₂ f₆₂₃ f₆₂₄ f₆₃₁ f₆₃₂ f₆₃₃ f₆₃₄

Each entry of the table, f_(ijk) represents, for a given input word i; how many times a context word C; and non-context word i; appears within a predetermined relationship. Thus, f₁₁₁-f₁₁₄ each represent the number of times the input word “Chicago” and the context word “to” appear within adjacencies of −2 words, −1 word, +1 word and +2 words respectively.

Based upon the frequencies, an N dimensional vector may be built for each input word (step 1020). The number of dimensions N of the frequency vector is a multiple of the total number of context words, the total number of input words and the total number of relations identified by the method 1000. The vector represents grammatical links that exist between the input words and the context words. Thus, each input word maps to an N dimensional frequency space. A representative frequency space is shown in FIG. 2 (N=3).

The method 1000 builds clusters of input words (Step 1030). According to the principles of the present invention, input words having the same lexical significance should possess similar vectors in the frequency space. Thus, it is expected that city names will exhibit frequency characteristics that are similar to each other but different from other input words having a different lexical significance. They will be included in a cluster (say, cluster 10, FIG. 2). So, too, with colors. They will be included in another cluster (say, cluster 20). Where words exhibit similar frequency significance, they are included within a single cluster.

As is known, a cluster may be represented in an N-dimensional frequency space by a centroid coordinate and a radius indicating the volume of the cluster. The radius indicates the “compactness” of the elements within a cluster. Where a cluster has a small radius, it indicates that the elements therein exhibit a very close relationship to each other in the frequency space. A larger radius indicates less similarities between elements in the frequency space.

The similarity between two words may be measured using the Manhattan distance metric between their feature vectors. Manhattan distance is based on the sum of the absolute value of the differences among the vector's coordinates. Alternatively, Euclidean and maximum metrics may be used to measure distances. Experimentally, the Manhattan distance metric was shown to provide better results than the Euclidean or maximum distance metrics.

Step 1030 may be applied recursively to grow clusters from clusters. That is, when two clusters are located close to one another in the N dimensional space, the method 1000 may enclose them in a single cluster having its own centroid and radius. The method 1000 determines a distance between two clusters by determining the distance between their centroids using one of the metrics discussed above with respect to the vectors of input words. Thus, the Manhattan, Euclidean and maximum distance metrics may be used.

A hierarchical “cluster tree” is grown representing a hierarchy of the clusters. At one node in the tree, the centroid and radius of a first cluster is stored. Two branches extend from the node to other nodes where the centroids and radii of subsumed clusters are stored. Thus, the tree structure maintains the centroid and radius of every cluster built according to Step 1030. Step 1030 recurs until a single, all encompassing cluster encloses all clusters and input words. This cluster is termed the “root cluster” because it is stored as the root node of the cluster tree. An exemplary cluster tree is shown in FIG. 3.

As will be appreciated, the root cluster N13 has a radius large enough to enclose all clusters and input words. The root cluster, therefore, possesses very little lexical significance. By contrast, “leaf clusters,” those provided at the ends of branches in the cluster tree, possess very strong lexical significance.

At Step 1040, the method 1000 cuts the cluster tree along a predetermined line in the tree structure. The cutting line separates large clusters from smaller clusters. The large clusters are discarded. What remains are smaller clusters, those with greater lexical significance.

The cutting line determines the number of clusters that will remain. One may use the median of the distances between clusters merged at the successive stages as a basis for the cutting line and prune the cluster tree at the point where cluster distances exceed this median value. Clusters are defined by the structure of the tree above the cutoff point.

Finally, the method 1000 ranks the remaining clusters (Step 1050). The lexical significance of a particular cluster is measured by its compactness value. The compactness value of a cluster simply may be its radius or an average distance of the members of the cluster from the centroid of the cluster. Thus, the tighter clusters exhibiting greater lexical significance will occur first in the ranked list of clusters and those exhibiting lesser lexical significance will occur later in the list. The list of clusters obtained from Step 1050 is a grammatical model of the input text.

The method 1000 is general in that it can be used to cluster “tokens” at any lexical level. For example, it may be applied to words and/or phrases. Table 2 illustrates the result of clustering words and Table 3 illustrates the result of clustering phrases as performed on an experimental set of training data taken from the How May I Help You? Training corpus disclosed in Gorin, et al., “How May I Help You?,” vol. 23, Speech Communication, pp. 113-127 (1997). Other lexical granularities (syllables, phonemes) also may be used.

TABLE 2 Results of Clustering Words from AT&T's How May I Help You? Corpus Class Compactness Index Value Class Members C363 0.131 make place C118 0.18 eight eighty five four nine oh one seven six three two zero C357 0.19 bill charge C260 0.216 an and because but so when C300 0.233 K O ok C301 0.236 from please C277 0.241 again here C202 0.252 as it's C204 0.263 different third C77 0.268 number numbers C275 0.272 need needed want wanted C256 0.274 assistance directory information C197 0.278 all before happened C68 0.278 ninety sixty C41 0.29 his our the their C199 0.291 called dialed got have as by in no not now of or something that that's there C27 0.296 whatever working C327 0.296 I I'm I've canada england france germany israel italy japan london C48 0.299 mexico paris C69 0.308 back direct out through C143 0.312 connected going it arizona california carolina florida georgia illinois island C89 0.314 jersey maryland michigan missouri ohio pennsylvania virginia west york C23 0.323 be either go see somebody them C90 0.332 about me off some up you

TABLE 3 Results from a First Iteration of Combining Phrase Acquisition and Clustering from the How May I Help You? Corpus [Words in a Phrase are Separated by a Colon]. Class Index Compactness Value Class Members D365 0.226 wrong:C77 second D325 0.232 C256:C256 C256 D380 0.239 area:code:C118:C118:C118:C118:C118 C68 D386 0.243 A:C77 this:C77 D382 0.276 C260:C357:C143:to:another C260:C357:C143:to:my:home D288 0.281 C327:C275:to:C363 I'd:like:toC363 to:363 yes:I'd:like:to:C363 D186 0.288 good:morning yes:ma'am yes:operator hello hi ma'am may well D148 0.315 problems trouble D87 0.315 A:T:C260:T C260:C327 C27:C27 C41:C77 C118 C143 C260 C197 C199 C202 C23 C260 C27 C277 C301 C69 C77 C90 operator to D183 0.321 C118:C118:hundred C204 telephone D143 0.326 new:C89 C48 C89 colorado massachusetts tennessee texas D387 0.327 my:home my:home:phone D4 0.336 my:calling my:calling:card my:card D70 0.338 C199:a:wrong:C77 misdialed D383 0.341 like:to:C363 trying:to:C363 would:like:to:C363 D381 0.347 like:to:C363:a:collect:call:to like:to:C363:collect:call would:like:to:C363:a:collect:call would:like:to:C363:a:collect:call would:like:to:C363:a:collect:call:to D159 0.347 C118:C118 C118:C118:C118 C118:C118:C118:C118:C118:C118 C118:C118:C118:C118:C118:C118:C118 C118:C118:C118:C118:C118:C118:C118:C118:C118:C118 C:118:C118:C118:C118:C118:C118:C118:C118:C118:C118:C118 area:code:C118:C118:C118 C300

Adjacency of words is but one relationship that the method 1000 may be applied to recognize from a corpus. More generally, however, the method 1000 may be used to recognize predetermined relationships among tokens of the corpus. For example, the method 1000 can be configured to recognize words that appear together in the same sentences or words that appear within predetermined positional relationships with punctuation. Taken even further, the method 1000 may be configured to recognize predetermined grammatical constructs of language, such as subjects and/or objects of verbs. Each of these latter examples of relationships may require that the method be pre-configured to recognize the grammatical constructs.

Several embodiments of the present invention are specifically illustrated and described herein. However, it will be appreciated that modifications and variations of the present invention are covered by the above teachings and within the purview of the appended claims without departing from the spirit and intended scope of the invention. 

We claim:
 1. A grammar learning method from a corpus, comprising: identifying context tokens within the corpus, for each non-context token in the corpus, counting occurrences of predetermined relationships of the non-context token to a context token, generating frequency vectors for each non-context token based upon the counted occurrences, and clustering non-context tokens based upon the frequency vectors, whereby the clusters of non-context tokens form a grammatical model of the corpus.
 2. The method of claim 1, wherein the clustering is performed based on Euclidean distances between frequency vectors.
 3. The method of claim 1, wherein the clustering is performed based on Manhattan distances between frequency vectors.
 4. The method of claim 1, wherein the clustering is performed based on maximum distance metrics between frequency vectors.
 5. The method of claim 1, wherein the frequency vectors of a non-context token are normalized based upon the number of occurrences of the non-context token in the corpus.
 6. The method of claim 1, wherein the frequency vectors are multi dimensional vectors, the number of dimensions determined by the number of context tokens and different levels of adjacency identified in the counting step.
 7. The method of claim 1, wherein clusters may be represented by a centroid vector.
 8. The method of claim 1, wherein the grammar learning method is unsupervised.
 9. The method of claim 1, wherein the corpus is a string of words in a language.
 10. A method of phrase grammar learning from a corpus, comprising: identifying context tokens within the corpus, for each non-context token in the corpus, counting occurrences of a predetermined relationships of a context token, generating frequency vectors for each non-context token based upon the counted occurrences, and clustering non-context tokens based upon the frequency vectors into a cluster tree, whereby the cluster tree forms a grammatical model of the corpus.
 11. The method of claim 10, wherein the clustering is performed based on Euclidean distances between frequency vectors.
 12. The method of claim 10, wherein the clustering is performed based on Manhattan distances between frequency vectors.
 13. The method of claim 10, wherein the frequency vectors of a non-context token are normalized based upon the number of occurrences of the non-context token in the corpus.
 14. The method of claim 10, wherein the frequency vectors are multi dimensional vectors, the number of dimensions determined by the number of context tokens and different levels of adjacency identified in the counting step.
 15. The method of claim 10, wherein clusters may be represented by a centroid vector.
 16. A method of phrase grammar learning from a corpus, comprising: identifying context words from a corpus, for each non-context word in the corpus, counting occurrences of the non-context word within a predetermined adjacency of a context word, generating frequency vectors for each non-context word based upon the counted occurrences, clustering non-context words based on the frequency vectors into a cluster tree, and cutting the cluster tree along a cutting line, thereby forming a grammatical model of the corpus.
 17. The method of claim 16, wherein the clustering is performed based on Euclidean distances between frequency vectors.
 18. The method of claim 16, wherein the clustering is performed based on maximum distance metrics between frequency vectors.
 19. The method of claim 16, wherein the frequency vectors of a non-context words are normalized based upon the number of occurrences of the non-context word in the corpus.
 20. The method of claim 16, wherein the frequency vectors are multi dimensional vectors, the number of dimensions determined by the number of context words and different levels of adjacency to context words to be recognized.
 21. The method of claim 16, wherein clusters may be represented by a centroid vector. 